Search Results for author:
Found 51 papers, 21 papers with code
NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions
Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors.
Source-free Depth for Object Pop-out
In this work, we adapt such depth inference models for object segmentation using the objects' ``pop-out'' prior in 3D.
Neural Radiance Fields for Manhattan Scenes with Unknown Manhattan Frame
Such representations are known to benefit from additional geometric and semantic supervision.
Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images
On the one hand, the proposed method learns to segment these planar hulls from the labeled data.
Robust RGB-D Fusion for Saliency Detection
In this work, we aim for RGB-D saliency detection that is robust to the low-quality depths which primarily appear in two forms: inaccuracy due to noise and the misalignment to RGB.
Gradient Obfuscation Checklist Test Gives a False Sense of Security
It has since become a trend to use these five characteristics as a sufficient test, to determine whether or not gradient obfuscation is the main source of robustness.
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Within the proposed benchmark, we explore some commonly known essentials of standard continual learning.
Spatially Multi-conditional Image Generation
However, multi-conditional image generation is a very challenging problem due to the heterogeneity and the sparsity of the (in practice) available conditioning labels.
Transforming Model Prediction for Tracking
Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function.
Ranked #1 on
Visual Object Tracking
on LaSOT
(IS metric)
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications.
Collapse by Conditioning: Training Class-conditional GANs with Limited Data
On the contrary, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability.
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers
We use linear layers with token-consistent stochastic parameters inside the multilayer perceptron blocks, without altering the architecture of the transformer.
End-to-End Learning of Multi-category 3D Pose and Shape Estimation
We use a Transformer-based architecture to detect the keypoints, as well as to summarize the visual context of the image.
Topology Preserving Local Road Network Estimation from Single Onboard Camera Image
We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points.
Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images
In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation
In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain.
GANmut: Learning Interpretable Conditional Space for Gamut of Emotions
On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability.
Boosting Crowd Counting with Transformers
This indicates that global scene context is essential, despite the seemingly bottom-up nature of the problem.
Unsupervised Compound Domain Adaptation for Face Anti-Spoofing
The proposed method successfully adapts to the compound target domain consisting multiple new spoof types.
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance.
Learning Target Candidate Association to Keep Track of What Not to Track
To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision.
Ranked #2 on
Visual Object Tracking
on OTB-2015
Unsupervised Robust Domain Adaptation without Source Data
This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data.
Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes.
Task Switching Network for Multi-Task Learning
Multiple tasks are performed by switching between them, performing one task at a time.
Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes
In this work, we present an unsupervised monocular framework for dense depth estimation of dynamic scenes, which jointly reconstructs rigid and non-rigid parts without explicitly modelling the camera motion.
An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain.
CompositeTasking: Understanding Images by Spatial Composition of Tasks
Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network.
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.
Soft Contrastive Learning for Visual Localization
Our soft assignment makes a gradual distinction between close and far images in both geometric and feature spaces.
Facial Emotion Recognition with Noisy Multi-task Annotations
In our formulation, we exploit a new method to enable the emotion prediction and the joint distribution learning in a unified adversarial learning game.
Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images
Image features for retrieval-based localization must be invariant to dynamic objects (e. g. cars) as well as seasonal and daytime changes.
Self-Calibration Supported Robust Projective Structure-from-Motion
In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints.
Geometrically Mappable Image Features
This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional.
Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets
This paper aims at learning category-specific 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category.
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing
Face anti-spoofing is a measure towards this direction for bio-metric user authentication, and in particular face recognition, that tries to prevent spoof attacks.
Divide-and-Conquer Adversarial Learning for High-Resolution Image and Video Enhancement
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
Convex Relaxations for Consensus and Non-Minimal Problems in 3D Vision
Notably, we further exploit the POP formulation of non-minimal solver also for the generic consensus maximization problems in 3D vision.
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Ranked #1 on
Video Generation
on TrailerFaces
Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map
The problem of localization often arises as part of a navigation process.
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution.
Sampling Algebraic Varieties for Robust Camera Autocalibration
This paper addresses the problem of robustly autocalibrating a moving camera with constant intrinsics.
Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length
In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with unknown focal length.
Model-free Consensus Maximization for Non-Rigid Shapes
In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using `rules' on measurements.
Face Translation between Images and Videos using Identity-aware CycleGAN
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement.
Improving Video Generation for Multi-functional Applications
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
Optimal Transformation Estimation With Semantic Cues
This paper addresses the problem of estimating the geometric transformation relating two distinct visual modalities (e. g. an image and a map, or a projective structure and a Euclidean 3D model) while relying only on semantic cues, such as semantically segmented regions or object bounding boxes.
Consensus Maximization With Linear Matrix Inequality Constraints
While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers.
Sliced Wasserstein Generative Models
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Robust and Optimal Sum-of-Squares-Based Point-to-Plane Registration of Image Sets and Structured Scenes
This paper deals with the problem of registering a known structured 3D scene and its metric Structure-from-Motion (SfM) counterpart.
LMI-Based 2D-3D Registration: From Uncalibrated Images to Euclidean Scene
This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates, and two or more uncalibrated cameras.
Efficient Pruning LMI Conditions for Branch-and-Prune Rank and Chirality-Constrained Estimation of the Dual Absolute Quadric
We present a new globally optimal algorithm for self-calibrating a moving camera with constant parameters.
